caa a22 4500 606159320 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1833-z doi (NATIONALLICENCE)springer-10.1007/s00521-015-1833-z Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System [Elektronische Daten] [Hossein Khanlou, Bee Ang, Mohsen Barzani, Mahyar Silakhori, Sepehr Talebian] An adaptive neuro-fuzzy system (ANFIS) model was employed to predict the surface roughness. Surface roughening of titanium biomaterials has a crucial effect on increasing the biocompatibility. For this purpose, sandblasted, large-grit, acid-etched (SLA) has been introduced as an effective method to change the surface texturing and roughness. Subsequent processes—polishing, sandblasting and acid etching or SLA—were employed to modify the surface. Alumina particles for surface blasting and Kroll's etchant (3ml HF+6ml HNO3+100ml H2O) for acid etching were utilized in this experiment. This was performed for three different periods of time (10, 20 and 30s) and temperatures (25, 45 and 60 centigrade). Correspondingly, the Ti-13Zr-13Nb surfaces were evaluated using a field emission scanning electron microscope for texturing, contact mode profile meter for the average surface roughness (Ra) (nm) and atomic force microscopy for surface texturing at the nano-scale. In addition, the surface roughness was reduced in each condition, particularly in extremely high conditions. Significantly, the ANFIS model predicted the Ra amount of textured surface with an error band of 10%. This research presents an idea to use the ANFIS model to obtain proper biological signs on the roughened surface in terms of surface roughness. The Natural Computing Applications Forum, 2015 ANFIS nationallicence Acid etching and sandblasting (SLA) nationallicence Biocompatibility nationallicence Titanium alloys nationallicence Surface roughness nationallicence Prediction and modeling nationallicence Surface characterization nationallicence Khanlou Hossein Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut Ang Bee Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut Barzani Mohsen Centre of Advance Manufacturing and Material Processing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut Silakhori Mahyar Centre of Advance Manufacturing and Material Processing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut Talebian Sepehr Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1751-1761 0941-0643 26:7<1751 2015 26 521 https://doi.org/10.1007/s00521-015-1833-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1833-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Khanlou Hossein Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Ang Bee Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Barzani Mohsen Centre of Advance Manufacturing and Material Processing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Silakhori Mahyar Centre of Advance Manufacturing and Material Processing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut NATIONALLICENCE 700 1- Talebian Sepehr Centre of Advanced Materials, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1751-1761 0941-0643 26:7<1751 2015 26 521